OpenRaiser/Intern-Atlas huggingface dataset:
Temporal Cross-Media Retrieval with Soft-Smoothing
Venue: arXiv cs.MM 2018
Year: 2018

Multimedia information have strong temporal correlations that shape the way modalities co-occur over time. In this paper we study the dynamic nature of multimedia and social-media information, where the temporal dimension emerges as a strong source of evidence for learning the temporal correlations across visual and textual modalities. So far, cross-media retrieval models, explored the correlations between different modalities (e.g. text and image) to learn a common subspace, in which semantically similar instances lie in the same neighbourhood. Building on such knowledge, we propose a novel temporal cross-media neural architecture, that departs from standard cross-media methods, by explicitly accounting for the temporal dimension through temporal subspace learning. The model is softly-constrained with temporal and inter-modality constraints that guide the new subspace learning task by favouring temporal correlations between semantically similar and temporally close instances. Experiments on three distinct datasets show that accounting for time turns out to be important for cross-media retrieval. Namely, the proposed method outperforms a set of baselines on the task of temporal cross-media retrieval, demonstrating its effectiveness for performing temporal subspace learning.
Improvement of K Mean Clustering Algorithm Based on Density
Venue: arXiv cs.LG 2018
Year: 2018

The purpose of this paper is to improve the traditional K-means algorithm. In the traditional K mean clustering algorithm, the initial clustering centers are generated randomly in the data set. It is easy to fall into the local minimum solution when the initial cluster centers are randomly generated. The initial clustering center selected by K-means clustering algorithm which based on density is more representative. The experimental results show that the improved K clustering algorithm can eliminate the dependence on the initial cluster, and the accuracy of clustering is improved.
Strategic Contention Resolution in Multiple Channels
Venue: arXiv cs.GT 2018
Year: 2018

We consider the problem of resolving contention in communication networks with selfish users. In a \textit{contention game} each of $n \geq 2$ identical players has a single information packet that she wants to transmit using one of $k \geq 1$ multiple-access channels. To do that, a player chooses a slotted-time protocol that prescribes the probabilities with which at a given time-step she will attempt transmission at each channel. If more than one players try to transmit over the same channel (collision) then no transmission happens on that channel. Each player tries to minimize her own expected \textit{latency}, i.e. her expected time until successful transmission, by choosing her protocol. The natural problem that arises in such a setting is, given $n$ and $k$, to provide the players with a common, anonymous protocol (if it exists) such that no one would unilaterally deviate from it (equilibrium protocol). All previous theoretical results on strategic contention resolution examine only the case of a single channel and show that the equilibrium protocols depend on the feedback that the communication system gives to the players. Here we present multi-channel equilibrium protocols in two main feedback classes, namely \textit{acknowledgement-based} and \textit{ternary}. In particular, we provide equilibrium characterizations for more than one channels, and give specific anonymous, equilibrium protocols with finite and infinite expected latency. In the equilibrium protocols with infinite expected latency, all players transmit successfully in optimal time, i.e. $Θ(n/k)$, with probability tending to 1 as $n/k \to \infty$.
Broadband Internet and Social Capital
Venue: arXiv cs.CY 2018
Year: 2018

We study how the diffusion of broadband Internet affects social capital using two data sets from the UK. Our empirical strategy exploits the fact that broadband access has long depended on customers' position in the voice telecommunication infrastructure that was designed in the 1930s. The actual speed of an Internet connection, in fact, rapidly decays with the distance of the dwelling from the specific node of the network serving its area. Merging unique information about the topology of the voice network with geocoded longitudinal data about individual social capital, we show that access to broadband Internet caused a significant decline in forms of offline interaction and civic engagement. Overall, our results suggest that broadband penetration substantially crowded out several aspects of social capital.
Revitalizing Copybacks in Modern SSDs: Why and How
Venue: arXiv cs.OS 2018
Year: 2018

For modern flash-based SSDs, the performance overhead of internal data migrations is dominated by the data transfer time, not by the flash program time as in old SSDs. In order to mitigate the performance impact of data migrations, we propose rCopyback, a restricted version of copyback. Rcopyback works like the original copyback except that only n consecutive copybacks are allowed. By limiting the number of successive copybacks, it guarantees that no data reliability problem occurs when data is internally migrated using rCopyback. In order to take a full advantage of rCopyback, we developed a rCopyback-aware FTL, rcFTL, which intelligently decides whether rCopyback should be used or not by exploiting varying host workloads. Our evaluation results show that rcFTL can improve the overall I/O throughput by 54% on average over an existing FTL which does not use copybacks.
Intrusion Detection Using Mouse Dynamics
Venue: arXiv cs.HC 2018
Year: 2018

Compared to other behavioural biometrics, mouse dynamics is a less explored area. General purpose data sets containing unrestricted mouse usage data are usually not available. The Balabit data set was released in 2016 for a data science competition, which against the few subjects, can be considered the first adequate publicly available one. This paper presents a performance evaluation study on this data set for impostor detection. The existence of very short test sessions makes this data set challenging. Raw data were segmented into mouse move, point and click and drag and drop types of mouse actions, then several features were extracted. In contrast to keystroke dynamics, mouse data is not sensitive, therefore it is possible to collect negative mouse dynamics data and to use two-class classifiers for impostor detection. Both action- and set of actions-based evaluations were performed. Set of actions-based evaluation achieves 0.92 AUC on the test part of the data set. However, the same type of evaluation conducted on the training part of the data set resulted in maximal AUC (1) using only 13 actions. Drag and drop mouse actions proved to be the best actions for impostor detection.
End-to-End Content and Plan Selection for Data-to-Text Generation
Venue: arXiv cs.CL 2018
Year: 2018

Learning to generate fluent natural language from structured data with neural networks has become an common approach for NLG. This problem can be challenging when the form of the structured data varies between examples. This paper presents a survey of several extensions to sequence-to-sequence models to account for the latent content selection process, particularly variants of copy attention and coverage decoding. We further propose a training method based on diverse ensembling to encourage models to learn distinct sentence templates during training. An empirical evaluation of these techniques shows an increase in the quality of generated text across five automated metrics, as well as human evaluation.
Leveraging local network communities to predict academic performance
Venue: arXiv cs.SI 2018
Year: 2018

For more than 20 years, social network analysis of student collaboration networks has focused on a student's centrality to predict academic performance. And even though a growing amount of sociological literature has supported that academic success is contagious, identifying central students in the network alone does not capture how peer interactions facilitate the spread of academic success throughout the network. Consequently, we propose novel predictors that treat academic success as a contagion by identifying a student's learning community, consisting of the peers that are most likely to influence a student's performance in a course. We evaluate the importance of these learning communities by predicting academic outcomes in an introductory college statistics course with 103 students. In particular, we observe that by including these learning community predictors, the resulting model is 68 times more likely to be the correct model than the current state-of-the-art centrality network models in the literature.
Probabilistic Clustering Using Maximal Matrix Norm Couplings
Venue: arXiv cs.LG 2018
Year: 2018

In this paper, we present a local information theoretic approach to explicitly learn probabilistic clustering of a discrete random variable. Our formulation yields a convex maximization problem for which it is NP-hard to find the global optimum. In order to algorithmically solve this optimization problem, we propose two relaxations that are solved via gradient ascent and alternating maximization. Experiments on the MSR Sentence Completion Challenge, MovieLens 100K, and Reuters21578 datasets demonstrate that our approach is competitive with existing techniques and worthy of further investigation.
Redirect2Own: Protecting the Intellectual Property of User-uploaded Content through Off-site Indirect Access
Venue: arXiv cs.CR 2018
Year: 2018

Social networking services have attracted millions of users, including individuals, professionals, and companies, that upload massive amounts of content, such as text, pictures, and video, every day. Content creators retain the intellectual property (IP) rights on the content they share with these networks, however, very frequently they implicitly grant them, a sometimes, overly broad license to use that content, which enables the services to use it in possibly undesirable ways. For instance, Facebook claims a transferable, sub-licensable, royalty-free, worldwide license on all user-provided content. Professional content creators, like photographers, are particularly affected. In this paper we propose a design for decoupling user data from social networking services without any loss of functionality for the users. Our design suggests that user data are kept off the social networking service, in third parties that enable the hosting of user-generated content under terms of service and overall environment (e.g., a different location) that better suit the user's needs and wishes. At the same time, indirection schemata are seamlessly integrated in the social networking service, without any cooperation from the server side necessary, so that users can transparently access the off-site data just as they would if hosted in-site. We have implemented our design as an extension for the Chrome Web browser, called Redirect2Own, and show that it incurs negligible overhead on accessing 'redirected' content. We offer the extension as free software and its code as an open-source project.
FEther: An Extensible Definitional Interpreter for Smart-contract Verifications in Coq
Venue: arXiv cs.PL 2018
Year: 2018

Blockchain technology adds records to a list using cryptographic links. Therefore, the security of blockchain smart contracts is among the most popular contemporary research topics. To improve the theorem-proving technology in this field, we are developing an extensible hybrid verification tool chain, denoted as FSPVM-E, for Ethereum smart contract verification. This hybrid system extends the proof assistants in Coq, a formal proof-management system. Combining symbolic execution with higher-order theorem-proving, it solves consistency, automation, and reusability problems by standard theorem-proving approaches. This article completes the FSPVM-E by developing its proof engine. FSPVM-E is an extensible definitional interpreter based on our previous work FEther, which is totally developed in the Coq proof assistant. It supports almost all semantics of the Solidity programing language, and simultaneously executes multiple types of symbols. FEther also contains a set of automatic strategies that execute and verify the smart contracts in Coq with a high level of automation. The functional correctness of FEther was verified in Coq. In standard tutorials, the execution efficiency of FEther far exceeded that of the interpreters developed in Coq.
Sequence-to-Sequence Models for Data-to-Text Natural Language Generation: Word- vs. Character-based Processing and Output Diversity
Venue: arXiv cs.CL 2018
Year: 2018

We present a comparison of word-based and character-based sequence-to-sequence models for data-to-text natural language generation, which generate natural language descriptions for structured inputs. On the datasets of two recent generation challenges, our models achieve comparable or better automatic evaluation results than the best challenge submissions. Subsequent detailed statistical and human analyses shed light on the differences between the two input representations and the diversity of the generated texts. In a controlled experiment with synthetic training data generated from templates, we demonstrate the ability of neural models to learn novel combinations of the templates and thereby generalize beyond the linguistic structures they were trained on.
Deep Bi-Dense Networks for Image Super-Resolution
Venue: arXiv cs.CV 2018
Year: 2018

This paper proposes Deep Bi-Dense Networks (DBDN) for single image super-resolution. Our approach extends previous intra-block dense connection approaches by including novel inter-block dense connections. In this way, feature information propagates from a single dense block to all subsequent blocks, instead of to a single successor. To build a DBDN, we firstly construct intra-dense blocks, which extract and compress abundant local features via densely connected convolutional layers and compression layers for further feature learning. Then, we use an inter-block dense net to connect intra-dense blocks, which allow each intra-dense block propagates its own local features to all successors. Additionally, our bi-dense construction connects each block to the output, alleviating the vanishing gradient problems in training. The evaluation of our proposed method on five benchmark datasets shows that our DBDN outperforms the state of the art in SISR with a moderate number of network parameters.
VIPL-HR: A Multi-modal Database for Pulse Estimation from Less-constrained Face Video
Venue: arXiv cs.CV 2018
Year: 2018

Heart rate (HR) is an important physiological signal that reflects the physical and emotional activities of humans. Traditional HR measurements are mainly based on contact monitors, which are inconvenient and may cause discomfort for the subjects. Recently, methods have been proposed for remote HR estimation from face videos. However, most of the existing methods focus on well-controlled scenarios, their generalization ability into less-constrained scenarios are not known. At the same time, lacking large-scale databases has limited the use of deep representation learning methods in remote HR estimation. In this paper, we introduce a large-scale multi-modal HR database (named as VIPL-HR), which contains 2,378 visible light videos (VIS) and 752 near-infrared (NIR) videos of 107 subjects. Our VIPL-HR database also contains various variations such as head movements, illumination variations, and acquisition device changes. We also learn a deep HR estimator (named as RhythmNet) with the proposed spatial-temporal representation, which achieves promising results on both the public-domain and our VIPL-HR HR estimation databases. We would like to put the VIPL-HR database into the public domain.
A cognitive radio ad hoc networks based disaster management scheme with efficient spectrum management, collaboration and interoperability
Venue: arXiv cs.NI 2018
Year: 2018

In this work, a disaster management scheme based on cognitive radio ad hoc network (CRAHN) has been presented. Disaster management has been a big problem for mankind for years. However, still not much research work has been presented on this problem. Technology has been employed in past few years to address this problem. Cognitive radio ad hoc network presents a viable solution for disaster management. It can be deployed rapidly without infrastructure and it solves the spectrum scarcity and congestion issues that arise during disaster. This paper presents a novel solution for disaster management. It provides a multi-layer perceptron (MLP) based disaster detection scheme based on WSN. To solve the spectrum scarcity problem, a MLP based spectrum management scheme has been proposed. In order to ensure collaboration among rescue workers during disaster, a novel service discovery scheme has been proposed. To ensure interoperability during communication, XML format has been recommended. A real-time GUI has been proposed to provide shared situation awareness to rescue workers and enabling better decision making. The proposed approach has been implemented in NS-2 simulator. The results show accurate disaster detection, efficient spectrum usage, and interoperability and collaboration among nodes with reduced latency.
Parity games and universal graphs
Venue: arXiv cs.GT 2018
Year: 2018

This paper is a contribution to the study of parity games and the recent constructions of three quasipolynomial time algorithms for solving them. We revisit a result of Czerwiński, Daviaud, Fijalkow, Jurdziński, Lazić, and Parys witnessing a quasipolynomial barrier for all three quasipolynomial time algorithms. The argument is that all three algorithms can be understood as constructing a so-called separating automaton, and to give a quasipolynomial lower bond on the size of separating automata. We give an alternative proof of this result. The key innovations of this paper are the notion of universal graphs and the idea of saturation.
Secrecy Energy Efficiency Optimization for Artificial Noise Aided Physical-Layer Security in OFDM-Based Cognitive Radio Networks
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we investigate the power allocation of primary base station (PBS) and cognitive base station (CBS) across different orthogonal frequency division multiplexing (OFDM) subcarriers for energy-efficient secure downlink communication in OFDM-based cognitive radio networks (CRNs) with the existence of an eavesdropper having multiple antennas. We propose a secrecy energy efficiency maximization (SEEM) scheme by exploiting the instantaneous channel state information (ICSI) of the eavesdropper, called ICSI based SEEM (ICSI-SEEM) scheme with a given total transmit power budget for different OFDM subcarriers of both PBS and CBS. As for the case when the eavesdropper's ICSI is unknown, we also propose an SEEM scheme through using the statistical CSI (SCSI) of the eavesdropper, namely SCSI based SEEM (SCSI-SEEM) scheme. Since the ICSI-SEEM and SCSI-SEEM problems are fractional and non-convex, we first transform them into equivalent subtractive problems, and then achieve approximate convex problems through employing the difference of two-convex functions approximation method. Finally, new two-tier power allocation algorithms are proposed to achieve $\varepsilon$-optimal solutions of our formulated ICSI-SEEM and SCSI-SEEM problems. Simulation results illustrate that the ICSI-SEEM has a better secrecy energy efficiency (SEE) performance than SCSI-SEEM, and moreover, the proposed ICSI-SEEM and SCSI-SEEM schemes outperform conventional SR maximization and EE maximization approaches in terms of their SEE performance.
The Impact of Annotation Guidelines and Annotated Data on Extracting App Features from App Reviews
Venue: arXiv cs.IR 2018
Year: 2018

Annotation guidelines used to guide the annotation of training and evaluation datasets can have a considerable impact on the quality of machine learning models. In this study, we explore the effects of annotation guidelines on the quality of app feature extraction models. As a main result, we propose several changes to the existing annotation guidelines with a goal of making the extracted app features more useful and informative to the app developers. We test the proposed changes via simulating the application of the new annotation guidelines and then evaluating the performance of the supervised machine learning models trained on datasets annotated with initial and simulated guidelines. While the overall performance of automatic app feature extraction remains the same as compared to the model trained on the dataset with initial annotations, the features extracted by the model trained on the dataset with simulated new annotations are less noisy and more informative to the app developers. Secondly, we are interested in what kind of annotated training data is necessary for training an automatic app feature extraction model. In particular, we explore whether the training set should contain annotated app reviews from those apps/app categories on which the model is subsequently planned to be applied, or is it sufficient to have annotated app reviews from any app available for training, even when these apps are from very different categories compared to the test app. Our experiments show that having annotated training reviews from the test app is not necessary although including them into training set helps to improve recall. Furthermore, we test whether augmenting the training set with annotated product reviews helps to improve the performance of app feature extraction. We find that the models trained on augmented training set lead to improved recall but at the cost of the drop in precision.
Learning to Reason
Venue: arXiv cs.AI 2018
Year: 2018

Automated theorem proving has long been a key task of artificial intelligence. Proofs form the bedrock of rigorous scientific inquiry. Many tools for both partially and fully automating their derivations have been developed over the last half a century. Some examples of state-of-the-art provers are E (Schulz, 2013), VAMPIRE (Kovács & Voronkov, 2013), and Prover9 (McCune, 2005-2010). Newer theorem provers, such as E, use superposition calculus in place of more traditional resolution and tableau based methods. There have also been a number of past attempts to apply machine learning methods to guiding proof search. Suttner & Ertel proposed a multilayer-perceptron based method using hand-engineered features as far back as 1990; Urban et al (2011) apply machine learning to tableau calculus; and Loos et al (2017) recently proposed a method for guiding the E theorem prover using deep nerual networks. All of this prior work, however, has one common limitation: they all rely on the axioms of classical first-order logic. Very little attention has been paid to automated theorem proving for non-classical logics. One of the only recent examples is McLaughlin & Pfenning (2008) who applied the polarized inverse method to intuitionistic propositional logic. The literature is otherwise mostly silent. This is truly unfortunate, as there are many reasons to desire non-classical proofs over classical. Constructive/intuitionistic proofs should be of particular interest to computer scientists thanks to the well-known Curry-Howard correspondence (Howard, 1980) which tells us that all terminating programs correspond to a proof in intuitionistic logic and vice versa. This work explores using Q-learning (Watkins, 1989) to inform proof search for a specific system called non-classical logic called Core Logic (Tennant, 2017).